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5,991 papers
The construction establishes that learning can change effective epistemic reach even when primitive affordances and deployment resources are held fixed, and opens a complementary evaluation question for learning systems: not only what they infer from available evidence, but what informative evidence experience teaches them to bring within reach.
Adaptive Doubly Robust (ADR) is proposed, which combines adaptive importance weighting with re- ward regression through a control-variate correction and establishes its unbiasedness when the true user behavior model is observed and characterize a sufficient condition under which it reduces vari- ance relative to AIPS.
An extensive fault-injection study covering both computational and memory faults across three T2V models and a representative benchmark reveals reliability risks in deployed T2V systems and motivates further research on improving fault resilience.
PAC-LLM is proposed, a phase-space-aware adaptive fusion framework for long-term chaotic time series forecasting powered by LLMs that leverages learned phase-space features and textual information to fully enable LLM's time series forecasting capacity.
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Control performance guarantee is first established by deriving tracking error bound for the in-network control architecture, while accounting for computational delays, and an explicit expression of the proposed event-trigger condition for exponentially stabilizable systems is derived.
Professional basketball is the case study, chosen for its data rather than the league, and five public sources are fuse into one per-shot dataset of 4.23M shots over 21 seasons, finding that the analytics tools of professional teams stay out of reach.
This work proposes an asynchronous cooperative learning strategy that explicitly accounts for prediction accuracy, query point variations and delay effects, and a distributed control law based on an adjoint MAS is developed to ensure the desired control performance.
CAS (causal active sequential experimentation), which targets evaluation to model-workload pairs and repeats the test as evidence accumulates, to ask whether one assignment stays optimal across every quality table consistent with the evidence.
BEACON is proposed, a tri-modal contrastive learning framework that aligns three complementary views of urban space: physical representations from AE embeddings, semantic representations from point-of-interest (POI) text, and human behavioral representations from hourly POI visitation, while keeping the deployed representation image-only.
A benchmark of multi-visit, multi-specialty patient records is introduced that evaluates long-context evidence retrieval, cross-time evidence aggregation, and cross-specialty clinical reasoning and proposes MedCache, a hybrid framework that constructs temporally valid patient memory and organizes evidence into overlapping specialty views.
It is shown that continual unlearning operations accumulate geometric constraints in parameter space, leading to saturated subspaces that restrict future updates, a critical barrier to the long-term reliability of machine unlearning systems and motivate the development of plasticity-preserving unlearning algorithms.
The proposed update incorporates the objective gradient inside the denoising step, yielding an inference-time method that uses only a pretrained denoiser and gradient evaluations and is analyzed as an inexact projected-gradient method for constrained optimization over learned feasible geometries.
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GPT-Lab
· gpt-lab.eu
Sep 3, 2026
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
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